The randomized clinical trial (RCT) is arguably the linchpin of the drug development process, and the results of a randomized clinical trial are almost always analyzed using some form of statistical hypothesis test. Most hypothesis tests used for analyzing clinical trials assume a population model for statistical inference, when in fact a randomization model is more consistent with the way randomized clinical trials are actually conducted. Failure to consider the randomization model when analyzing clinical trials can lead to effective drugs being declared ineffective, and ineffective drugs being declared effective. In order to analyze clinical trials in accordance with the randomization model, sophisticated software for conducting permutation tests is needed. The overall goal of this research is to develop flexible and robust software, usable by statisticians or other medical data analysts, for conducting permutation tests for single- or multi-clinic randomized clinical trials. The ongoing advances in computing technology have created a favorable climate for development of software for conducting permutation tests. This project includes a collaboration with Dr. Rosenberger of the University of Maryland, Baltimore County who is a recognized expert on randomization based inference and adaptive designs. PROPOSED COMMERCIAL APPLICATIONS: Software that can use general permutation tests to analyze clinical trial data would have clear commercial value to clinical research organizations in academia, Government, and the pharmaceutical, biotechnology, and medical device industries. Key applications are the analysis of clinical trials with unusual randomization schemes, trials with unusual patterns of treatment response, and trials where standard distributional assumptions are invalid.